The present invention relates generally to vehicle crash discrimination systems, and more particularly to a method of modeling crash waveforms for evaluating and/or testing vehicle crash discrimination systems.
In the past, a small finite set of crash waveforms were typically generated for a particular vehicle model by actually crashing the vehicle under different conditions (i.e., vehicle speed, crash location, etc.). These finite sets of crash waveforms were generally used to represent all possible crash situations when testing and/or developing crash discrimination systems.
However, small finite sets of crash waveforms do not provide a reliable or realistic representation of all crash scenarios which can occur in real world situations. Thus, crash discrimination systems calibrated, or tested, using these finite sets of crash waveforms are possibly unreliable over the entire range of possible crash scenarios. Simply increasing the number of vehicles actually crashed to increase the finite sets of crash waveforms is not a realistic solution due to the extreme cost of crashing vehicles. Further, each crash involving the same vehicle and crash scenario typically generates statistically variant crash waveforms.